Through time series analysis, it is possible to obtain significant statistics and other necessary data characteristics. Prediction of time series allows predicting future values based on previously observed values. The exact prognosis of the time series is very important for a number of different areas, such as transport, energy, finance, economics, etc. It is within the topic of economy that the analysis and prediction of time series can also be used for exchange rates. The exchange rate itself can greatly affect the whole foreign trade. The aim of this article is therefore to analyze the exchange rate development of two currencies by analyzing time series through artificial neural networks. Experimental results show that neural networks are potentially usable and effective for exchange rate prediction.

The contribution deals with the economic value added and its influence on credit absorption capacity. The aim was to determine the significance of the difference between the economic value added (EVA) entity and EVA equity indicators on credit absorption by the construction sector in the Czech Republic. The data came from the Albertina database of Bisnode Czech Republic for the period 2012–2018; small and medium-sized enterprises, in particular, were selected. The most important factor for calculating the amount of credit absorption depends on the EVA entity indicator and the weighted average cost of capital. The calculations produced negative values for credit absorption, which reflects an unattractive investment climate for business owners and their creditors. In other words, loans sought by enterprises in the Czech construction sector do not lead to a greater degree of realization of their goals, i.e., an increase in value for shareholders.

Abstract.A remarkable success depends on a variety of factors, one of which is the ability to motivate the employees, increasing motivation of managers, measuring business processes and measuring and observing values of the company. The tangible results are also shown by the indicator of return on equity (ROE). The article aims at reflecting the importance of ROE for the EVA Equity (economic value added for shareholders) calculation of Motor Jikov Strojírenská, a.s. The data come from Albertina database. These are details of financial statements from 2000-2015. As a matter of fact, weighted average costs of capital, alternative cost value of equity capital and EVA Equity are calculated. Software Statistica and its sophisticated tool data mining -automated neural networks was used for finding a correlation between EVA Equity indicator and ROE. In addition, 10,000 neural networks were generated, five of which with the best results have been stored. The results show that EVA Equity is not dependent on the ROE rate.

Mutual trade restrictions between the USA and the PRC caused by the USA feeling of imbalance of trade between these two countries have significantly influenced not only the trade between these two states but also the overall atmosphere of the international trade in the last few years. The objective of the contribution is to find out whether machine learning forecasting is capable of equalizing time series so that the model effectively forecasts the future development of the time series even in the context of an extraordinary situation caused by such factors as the mutual sanctions of the USA and PRC. The dataset shows the course of the time series at monthly intervals starting from January 2000 to June 2019. There is regression carried out using neural structures. Three sets of artificial neural networks are generated. They are differ in the considered time series lag. 10,000 neural networks are generated, out of which 5 with the best characteristics are retained. The mutual USA and PRC sanctions did not affect the success rate of the machine learning forecasting of the CR import from the PRC. It is evident that the mutual sanctions shall affect the trade between the CR and the PRC.

Abstract. Thanks to public debt, nations maintain a stable tax rate and at the same time raise expenses for suppressing budget crises. Public debt is considered risk-free and is issued by the national government. However, extensive public debt raises interest rates, extrudes private investments, worsens fixed balance, widens short-term fluctuation and has a negative impact on economic growth. The performance of the economy can be measured mostly by GDP, which is expressed as the monetary value of all finished products and services produced in a certain time period in one country. The aim of this paper is to explore the relationship, or rather identify the mutual correlation of public debt of one country and its performance (GDP). The research includes countries of the European Union, which have an identifiable timeline of public debt and GDP. The source of this data is the database of the World Bank. The information is from the period of years from 1995 through 2015. The correlation of the whole file of countries and then individual countries is ascertained. On the basis of the results, we can state that in the explored sample of countries, high dependence between public debt and GDP has been proven.